store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_177989', 'seed': 177989, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[15198, 2617, 40848, 57960, 58841, 21164, 38998, 32691, 28706, 50889, 36544, 58025, 597, 29048, 43912, 1082, 35237, 26491, 6155, 45555]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.5957, 'nll': 2.6414893196105957}, 'chosen_samples': [15432, 49103, 31191, 34428, 47825, 27077, 10639, 9250, 28462, 2204, 51114, 37302, 10869, 616, 41737, 52830, 21770, 12934, 42408, 57984, 49987, 44086, 42426, 11494, 32917, 10856, 5872, 3657, 32587, 2023, 1045, 53854, 16920, 22965, 52753, 4757, 35333, 58036, 35113, 32421], 'chosen_samples_score': ['1.0662513', '1.0793812', '1.096531', '1.0679114', '1.0888174', '1.0806272', '1.0664287', '1.095638', '1.0831587', '1.0817136', '1.0873494', '1.1077728', '1.0690724', '1.0688028', '1.0710928', '1.0927045', '1.0827887', '1.0759385', '1.0779209', '1.0998285', '1.083061', '1.086288', '1.0706272', '1.1113856', '1.1123577', '1.1136714', '1.2075088', '1.1638157', '1.1123626', '1.1530559', '1.2383459', '1.1760583', '1.1800158', '1.2483778', '1.130682', '1.1379211', '1.1942146', '1.1882362', '1.131624', '1.1187199']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6901, 'nll': 1.6056649547576904}, 'chosen_samples': [27027, 53232, 54125, 22211, 48471, 50479, 43449, 53998, 39482, 48668, 40853, 35653, 54136, 29132, 40571, 13612, 37900, 19432, 21851, 45889, 38112, 4893, 42789, 53954, 20015, 27219, 16176, 34481, 3647, 59177, 19931, 42467, 35509, 14095, 45207, 38932, 12304, 1328, 20869, 38282], 'chosen_samples_score': ['0.95474434', '0.95763737', '0.9579765', '0.9616285', '0.9582661', '0.96203524', '0.9831358', '0.97268045', '0.9786713', '0.96764964', '0.97369415', '0.97443056', '0.9725799', '0.9670848', '0.9630371', '0.9731821', '0.9719728', '0.96704566', '0.9936', '1.019923', '1.0647916', '1.0273373', '1.0020926', '1.0291352', '1.0685012', '0.99678147', '1.0285535', '1.0028279', '1.0664488', '1.0335536', '1.2190847', '1.0516894', '0.99936175', '1.0041245', '1.0709388', '1.0279417', '1.0042113', '1.0903703', '1.0422001', '1.0009859']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.751, 'nll': 1.2262862634658813}, 'chosen_samples': [1374, 16379, 8390, 11516, 47685, 7643, 53872, 42658, 38898, 40843, 35523, 26376, 2856, 52294, 18398, 22583, 39429, 40678, 5017, 36573, 19111, 189, 1881, 52095, 49571, 19847, 46448, 27413, 40654, 12555, 17849, 40492, 56457, 29390, 59446, 44040, 13061, 5106, 25384, 8257], 'chosen_samples_score': ['0.7873254', '0.78757197', '0.7953881', '0.7936765', '0.79019547', '0.78776515', '0.79229975', '0.796073', '0.797874', '0.79612434', '0.7996197', '0.8070655', '0.81554574', '0.8040569', '0.8066312', '0.8115672', '0.8019276', '0.8022424', '0.8118161', '0.80032295', '0.8207714', '0.8229605', '0.82584804', '0.83172846', '0.83117306', '0.8298878', '0.8325666', '0.9035042', '0.8326435', '0.8553816', '0.8887313', '0.8532558', '0.9342633', '0.8330105', '0.8443877', '0.8503388', '0.8439166', '0.85465974', '0.8815108', '0.8852812']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.755, 'nll': 1.3133885005950927}, 'chosen_samples': [56627, 21461, 58249, 9659, 1924, 40560, 6755, 54763, 7919, 34184, 46682, 57541, 33429, 14125, 21224, 20185, 21174, 11038, 38557, 53126, 10683, 50149, 52620, 14249, 19360, 16152, 45616, 57459, 56212, 58543, 34886, 49438, 21307, 8042, 34285, 2804, 2447, 29713, 40006, 53280], 'chosen_samples_score': ['0.66975427', '0.6705011', '0.6708053', '0.6725452', '0.68726015', '0.760998', '0.7242597', '0.71478695', '0.68238086', '0.6913619', '0.752576', '0.6773461', '0.6914635', '0.6908796', '0.680954', '0.677778', '0.7623844', '0.7335018', '0.6773022', '0.739123', '0.675636', '0.6803133', '0.6859981', '0.6940799', '0.6900161', '0.7212713', '0.7056636', '0.6730369', '0.71945083', '0.688504', '0.7982928', '0.78015083', '0.6774154', '0.7467326', '0.69158864', '0.6945306', '0.6708618', '0.68941677', '0.6923672', '0.7057419']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8512, 'nll': 0.8873850177764893}, 'chosen_samples': [40599, 53344, 49582, 9440, 59927, 9180, 13120, 1642, 39473, 33838, 45094, 18486, 37347, 46439, 34115, 38974, 14705, 59615, 59314, 12142, 46892, 55556, 13827, 27916, 17379, 46163, 22805, 45774, 37034, 11983, 14760, 44381, 41259, 49517, 28466, 19915, 50848, 21700, 36758, 14654], 'chosen_samples_score': ['0.82539034', '0.82732105', '0.8366517', '0.8395621', '0.8302014', '0.83738714', '0.8326179', '0.84435576', '0.83668876', '0.8450673', '0.8471581', '0.8452516', '0.85563105', '0.8471104', '0.8464912', '0.8452875', '0.8525644', '0.8461451', '0.84971315', '0.85834616', '0.9835323', '0.87092096', '0.8881089', '0.8785625', '0.8749648', '0.88011515', '0.8981132', '0.8631522', '0.8642673', '0.8921166', '0.8747754', '0.88035417', '0.8613128', '1.005368', '0.92527294', '0.8753186', '0.8990825', '1.0006061', '0.8831329', '0.92304593']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9112, 'nll': 0.5866823738098145}, 'chosen_samples': [19555, 10267, 8680, 52395, 59114, 33377, 42875, 809, 13007, 38311, 57335, 52819, 8379, 34188, 1075, 56007, 15789, 8189, 5375, 47949, 455, 59289, 19507, 49537, 30043, 9399, 17178, 28226, 57972, 22139, 24716, 3831, 10565, 29673, 8202, 14164, 2252, 46275, 9973, 47869], 'chosen_samples_score': ['0.88684636', '0.887062', '0.8889839', '0.8877517', '0.8894414', '0.8920779', '0.89203215', '0.8907264', '0.9008726', '0.9043463', '0.90176654', '0.90106076', '0.9236796', '0.95737195', '1.0618169', '0.90711015', '0.95516616', '0.9070748', '0.9205323', '1.07146', '1.0150108', '0.9322062', '0.90706605', '0.90199196', '0.9105641', '1.1223859', '0.9371227', '0.91884476', '0.94768834', '0.9720056', '0.9314831', '0.9161622', '0.922682', '0.97602516', '0.9041576', '0.91909176', '0.9011638', '0.9439949', '0.9954327', '0.91642946']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9342, 'nll': 0.437729883480072}, 'chosen_samples': [49890, 1330, 35326, 18824, 5684, 52688, 14733, 17079, 47260, 40046, 2450, 45800, 12127, 21804, 17792, 45048, 46580, 3719, 51371, 41527, 14573, 18951, 19642, 10256, 22497, 25910, 644, 36818, 17895, 8287, 54880, 44271, 39668, 23782, 5129, 18501, 16572, 10028, 50743, 40708], 'chosen_samples_score': ['0.840037', '0.8465867', '0.84048295', '0.8466244', '0.8491771', '0.8647464', '0.85072947', '0.8603759', '0.8698024', '0.8689712', '0.8606544', '0.85185224', '0.85017043', '0.8613971', '0.86879486', '0.8700655', '0.9189818', '0.903858', '0.92140555', '0.9589184', '1.0891296', '0.9337462', '0.9482053', '0.910224', '0.9020682', '0.9299022', '0.9068665', '0.87775284', '0.88061786', '0.8891951', '0.9054688', '0.96159935', '0.9019169', '0.8882857', '1.0063334', '0.87324697', '1.0160661', '0.8718731', '0.90965784', '0.9109692']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9408, 'nll': 0.40658051471710205}, 'chosen_samples': [8459, 4139, 20217, 18324, 37078, 13365, 26852, 6474, 15515, 44800, 19892, 49545, 3268, 12317, 55331, 14769, 51544, 43559, 2761, 32473, 38589, 34819, 8031, 1453, 44865, 54316, 59919, 29179, 47560, 43042, 40547, 17129, 41933, 20150, 15595, 39451, 36417, 14650, 30123, 32375], 'chosen_samples_score': ['0.90470064', '0.9365737', '0.9291222', '0.9296652', '0.9699747', '0.9172644', '0.94757324', '0.9282595', '0.9600591', '0.9449182', '0.9180825', '0.94161385', '0.92611253', '0.96436435', '0.9068074', '0.9430466', '0.9369468', '0.91875374', '0.9651271', '0.9177741', '0.92576694', '0.9115755', '0.95783573', '0.9073733', '0.91510475', '0.9147232', '0.954975', '0.97426695', '1.085378', '0.9841963', '0.99739486', '0.98845166', '1.0370934', '0.9755536', '0.97784585', '1.1025403', '1.0045693', '0.9831987', '1.0316362', '1.1669124']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9542, 'nll': 0.37313807344436645}, 'chosen_samples': [11842, 47690, 51993, 46346, 9402, 1573, 7793, 14629, 11858, 15618, 49895, 55314, 4414, 9433, 29765, 49004, 35406, 25246, 54981, 36744, 28654, 14266, 17855, 42662, 2092, 13156, 57463, 42746, 18564, 44298, 37469, 59726, 32776, 2423, 26898, 40312, 17521, 51179, 4165, 4111], 'chosen_samples_score': ['0.89575523', '0.8975476', '0.9590785', '0.9190003', '0.921688', '0.90392363', '0.9072097', '0.9403988', '0.9143762', '0.9341451', '0.9102618', '0.91570425', '0.8981153', '0.90080535', '0.93067765', '0.89830697', '0.9061704', '0.9471048', '0.9448836', '0.94836324', '0.9334943', '0.9145429', '0.9120201', '0.91610754', '0.9082065', '0.90368277', '0.92119265', '0.9676832', '1.0442492', '0.97423786', '1.0031272', '1.0859146', '0.9867749', '1.1756887', '1.0044949', '1.0479547', '1.0063517', '0.9870566', '1.0817188', '0.97396666']})
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store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9633, 'nll': 0.3033057005882263}, 'chosen_samples': [9448, 9555, 19124, 32256, 7264, 6174, 39151, 49859, 24462, 37060, 49889, 19322, 36434, 43898, 11747, 12792, 16951, 4822, 9731, 46655, 7833, 1148, 39172, 36402, 16600, 23478, 8297, 49893, 20792, 26483, 32880, 30900, 10850, 1674, 23423, 15781, 56006, 25310, 44328, 56654], 'chosen_samples_score': ['0.9083872', '0.9093352', '0.91090745', '0.909395', '0.91210157', '1.0180718', '1.0537244', '1.0046346', '0.9633496', '0.94622386', '1.0205303', '0.95206136', '0.91465497', '0.9655837', '0.9770514', '0.94534427', '0.9552567', '1.0907903', '0.96500957', '0.95702106', '1.075978', '0.94321316', '1.0112635', '0.95630646', '0.9630334', '0.9340616', '0.9982842', '0.920769', '0.91832453', '0.9605558', '1.1257634', '0.9385557', '0.9584687', '0.95619917', '0.92391723', '0.9144899', '0.9809472', '1.0203755', '0.9172424', '0.9298943']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9687, 'nll': 0.25842676129341124}, 'chosen_samples': [5638, 34328, 23136, 25803, 14935, 55531, 3056, 34942, 29303, 28368, 11600, 41084, 11572, 13538, 58829, 24860, 31748, 56454, 46164, 26135, 42774, 51432, 41299, 9340, 24589, 8458, 10982, 46734, 23140, 602, 14722, 11292, 39602, 34761, 39355, 55042, 20280, 44261, 17603, 2845], 'chosen_samples_score': ['0.76882935', '0.7702742', '0.7754593', '0.77642816', '0.77507186', '0.7764517', '0.7784457', '0.77970284', '0.77961445', '0.7777869', '0.78024614', '0.7829886', '0.7913226', '0.7930108', '0.7855276', '0.801713', '0.7992133', '0.7928027', '0.7862668', '0.7868855', '0.8077983', '0.8407071', '0.8209251', '0.8749917', '0.8499824', '0.81816155', '0.857864', '0.81741434', '0.89760035', '0.8326674', '0.8171163', '0.81816894', '0.8481805', '0.843509', '0.81491226', '0.8401365', '0.82027537', '0.8317433', '0.8286503', '0.86051965']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9634, 'nll': 0.29830326323509215}, 'chosen_samples': [59400, 45666, 33484, 32668, 12406, 41744, 18031, 22169, 9118, 50946, 57665, 52516, 37962, 18487, 53547, 3030, 13259, 43942, 29530, 38397, 10744, 54994, 5315, 41834, 35654, 32897, 21896, 47828, 24219, 11074, 17494, 31664, 53980, 32445, 20784, 31301, 36337, 23962, 19942, 14602], 'chosen_samples_score': ['0.7775189', '0.7803565', '0.78266585', '0.78784126', '0.7881384', '0.78885216', '0.7925458', '0.798697', '0.818754', '0.80529195', '0.8760836', '0.81782824', '0.88113', '0.9807375', '0.8124967', '0.8247392', '0.83878183', '0.791952', '0.8376708', '0.7945924', '0.7926251', '0.8771319', '0.83286', '0.8630099', '0.9296182', '0.7914722', '0.8180116', '0.88000685', '0.8789823', '0.9363274', '0.79989237', '0.85884875', '0.9386389', '0.7990455', '0.86674887', '0.8183679', '0.8109739', '0.8361456', '0.9692851', '0.79621303']})
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